An Approximate Forecasting of Electricity Load and Price of a Smart Home Using Nearest Neighbor

  • Muhammad Nawaz
  • Nadeem JavaidEmail author
  • Fakhar Ullah Mangla
  • Maria Munir
  • Farwa Ihsan
  • Atia Javaid
  • Muhammad Asif
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 993)


In Smart Grid, electricity demand and price forecasting literature has focused on Industrial, Buildings, and Residential sector demand, but this paper focuses on short term electricity demand and price forecasting for residential customer. Here we take smart meter data of hourly based from a smart home. First standardize and selected important features by using Recursive Feature Elimination with Linear Support Vector Classifier (RFE-LSVC). Second, do forecasting through K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Regression (SVR) models and perform comparative analysis among models against four scenarios and provided best solution among all for individual scenario. This work proposed best solution of smart home’s load and price forecasting for smart grid to manage demand response efficiently. We evaluated every Models with Mean Absolute Percentage Error (MAPE).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Muhammad Nawaz
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Fakhar Ullah Mangla
    • 2
  • Maria Munir
    • 2
  • Farwa Ihsan
    • 2
  • Atia Javaid
    • 1
  • Muhammad Asif
    • 3
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of SargodhaSargodhaPakistan
  3. 3.The Islamia University of BahawalpurBahawalpurPakistan

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